IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks
Abstract Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. M...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-38887-z |
| _version_ | 1797769604557176832 |
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| author | Ashraf Aboshosha Ayman Haggag Neseem George Hisham A. Hamad |
| author_facet | Ashraf Aboshosha Ayman Haggag Neseem George Hisham A. Hamad |
| author_sort | Ashraf Aboshosha |
| collection | DOAJ |
| description | Abstract Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured. |
| first_indexed | 2024-03-12T21:11:25Z |
| format | Article |
| id | doaj.art-dde8150bfc7440a583b5d088c3a4cd00 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T21:11:25Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dde8150bfc7440a583b5d088c3a4cd002023-07-30T11:11:32ZengNature PortfolioScientific Reports2045-23222023-07-0113111310.1038/s41598-023-38887-zIoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networksAshraf Aboshosha0Ayman Haggag1Neseem George2Hisham A. Hamad3Rad. Eng. Dept., NCRRT, Egyptian Atomic Energy Authority (EAEA)Electronics Technology Department, Faculty of Technology and Education, Helwan UniversityRad. Eng. Dept., NCRRT, Egyptian Atomic Energy Authority (EAEA)Electronics Technology Department, Faculty of Technology and Education, Helwan UniversityAbstract Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured.https://doi.org/10.1038/s41598-023-38887-z |
| spellingShingle | Ashraf Aboshosha Ayman Haggag Neseem George Hisham A. Hamad IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks Scientific Reports |
| title | IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks |
| title_full | IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks |
| title_fullStr | IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks |
| title_full_unstemmed | IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks |
| title_short | IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks |
| title_sort | iot based data driven predictive maintenance relying on fuzzy system and artificial neural networks |
| url | https://doi.org/10.1038/s41598-023-38887-z |
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